Deep learning models for breast tumor segmentation in DCE-MRI may exhibit disparities in performance across demographic and clinical subgroups, raising concerns about fairness and clinical trustworthiness. In this work, we propose a subgroup-aware in-processing mitigation strategy that integrates divergence-based regularization directly into the training loop. By leveraging interpretable metadata (e.g., age, menopausal status, breast density), we identify subgroups where the model underperforms and assign higher loss weights to these samples in proportion to their divergence from average performance. Our method enables the model to focus training on underrepresented or harder-to-segment subpopulations without requiring external data or post-processing correction. We evaluate our approach on the MAMA-MIA 2025 challenge dataset, demonstrating improvements in both overall segmentation quality and fairness score. Our results highlight the potential of in-processing mitigation as an effective and practical pathway toward equitable medical image segmentation.